Friday, 21 June 2013

Java Concurrent Counters By Numbers

{UPDATE 03/09/14: If you come here looking for JMH related content start at the new and improved JMH Resources Page and branch out from there!}It is common practice to use AtomicLong as a concurrent counter for reporting such metrics as number of messages received, number of bytes processed etc. Turns out it's a bad idea in busy multi-threaded environments... I explore some available/up-and-coming alternatives and present a proof of concept for another alternative of my own.
When monitoring an application we often expose certain counters via periodical logging, JMX or some other mechanism. These counters need to be incremented and read concurrently, and we rely on the values they present for evaluating load, performance or similar aspects of our systems. In such systems there are often business threads working concurrently on the core application logic, and one or more reporting/observing threads which periodically sample the event counters incremented by the business threads.
Given the above use case, lets explore our options (or if you are the impatient kind skip to the summary and work your way backwards).

Atomic Mother: too much love

We can use AtomicLong, and for certain things it's a damn fine choice. Here's what happens when you count an event using AtomicLong (as of JDK7, see comments for JDK8 related discussion):

This is a typical CAS retry loop. To elaborate:

Volatile read current value

Increment

Compare and swap current with new

If we managed to swap, joy! else go back to 1

Now imagine lots of threads hitting this code, how will we suffer?

We could loop forever (in theory)!

We are bound to experience the evil step sister of false sharing, which is real sharing. All writer threads are going to be contending on this memory location and thus one cache line and digging their heels until they get what they want. This will result is much cache coherency traffic.

We are using CAS (translates to lock cmpxchg on intel, read more here) which is a rather expensive instruction (see the wonderful Agner reference tables). This is much like a volatile write in essence.

But the above code also has advantages:

As it says on the tin, this is an atomic update of the underlying value. That value will go through each sequential value and will only continue execution once we ticked that value.

It follows that values returned by this method are unique.

The code is readable, the complex underside neatly cared for by the JMM(Java Memory Model) and a CAS.

There's nothing wrong with AtomicLong, but it seems like it gives us more than we asked for in this use case, and thus delivers less in the way of writer performance. This is your usual tradeoff of specialisation/generalisation vs performance. To gain performance we can focus our requirements a bit:

Writer performance is more important than reader performance: We have many logic threads incrementing, they need to suffer as little as possible for the added reporting requirement.

We don't require a read for each write, in fact we're happy with just incrementing the counters: We can live without the atomic joining of the inc and the get.

We're willing to sacrifice the somewhat theoretical accuracy of the read value if it makes things better: I say theoretical because at the end of the day if you are incrementing this value millions of times per second you are not going to report on each value and if you try and capture the value non-atomically then the value reported is inherently inaccurate.

The above use case is in fact benchmarked in the Asymmetric example bundled with JMH. Running a variation with N writers and one reader we get the following (code is here):AtomicCounterBenchmark.rw:get(N=1) - 4.149 nsec/opAtomicCounterBenchmark.rw:inc(N=1) - 105.439 nsec/op

Cry beloved writers! the reader pays nothing while our important business threads thrash about! If you are the sort of person who finds joy in visualising concurrent behaviour think of 11 top paid executives frantically fighting over a pen to fill in an expenses report to be read by some standards committee.

WARNING: Hacks & Benchmarks ahead!

In order to make the JDK8 classes used below run on JDK7 and in order to support some of my own code used below I had to run the benchmarks with my own Thread class. This led to the code included being a hack of the original JDK8 code. A further hack was required to make JMH load my thread factory instead of the one included in JMH. The JMH jar included is my hacked version and the changed class is included in the project as well. All in the name of experimentation and education...

The benchmarks were all run on an intel Xeon dual socket, 24 logical core machine. Because of the way JMH groups work (you have to specify the exact number of threads for asymmetric tests) I benchmarked on the above setup and did not bother going further(1/11/23 writers to 1 reader). I did not benchmark in situations were the number of threads exceeds the number of cores. Your mileage may wildly vary etc, etc, etc. The code is on GitHub as well as the results summary and notes on how the benchmarks were run.

The Long Adder

If ever there was a knight in shining armour for all things java concurrency it is Doug Lea, and Doug has a solution to the above problem coming to you in JDK 8 (or sooner if you want to grab it and use it now) in the form of LongAdder (extends Striped64). LongAdder serves the use case illustrated above by replacing the above increment logic with something a bit more involved:

Well... alot more involved really. 'Whatup Doug?' you must be asking yourself (I assume, maybe it makes instant sense to you, in which case shut up smart asses). Here's a quick break down:

Look! No contention!

LongAdder has a field called cells which is an array of Cells whose length is a power of 2, each Cell is a padded against false sharing volatile long (padding is counting on field order, they should probably have used inheritance to enforce that, like JMH's BlackHole). Kudos to Doug for the padded cells pun!

LongAdder has a field called base which is a volatile long (no padding here),

if ((as = cells) != null || !casBase(b = base, b + x)) - Load the volatile cellsfield into a local variable. If it's not null keep going. If it is null try and CAS the locally read value of base with the incremented value. In other words, if we got no cells we give the old AtomicLong method a go. If there's no contention this is where it ends. If there is contention, the plot thickens...

So we've either hit contention now or before, our next step is to find our thread's hash, to be used to find our index in the cells array.

!(uncontended = a.cas(v = a.value, v + x)) - we got a cell and it's not null, try and CAS it's value to the new value.

So... we either have no cell for this thread, or we failed to increment the value for this thread, lets call longAccumulate(x, null, uncontended) where the plot thickens

If you thought the above was complex, longAccumulate will completely fry your noodle. Before I embark on explaining it, let us stop and admire the optimisations which go into each line of code above, in particular note the careful effort made to avoid redundant reads of any data external to the function and the need to cache locally all values of volatile reads.
So... what does longAccumulate do? The code explains itself:

Got it? Let's step through (I'm not going to do line by line for it, no time):

Probe is a new field on Thread, put there to support classes which require Thread id hashing. It's initialised/accessed via ThreadLocalRandom and used by ForkJoin Pool and ConcurrentHashMap (This is JDK8, none of it happens yet in JDK7 land), getProbe is stealing it directly from Thread using Unsafe and field offset, cheeky! in my version I added a field to my CounterThread to support this.

If there's no array we create it. The cells array creation/replacement is a contention point shared between all threads, but should not happen often. We use cellsBusy as a spin lock. The cells array is sort of a copy on write array if you will, but the write spin lock is not the array, it's cellsBusy.

If there's no cell at our index we create one. We use cellsBusy to lock for writing to the table.

If we contend on the cell we expand the cells array, the array is only expanded if it's smaller than the number of available CPUs.

If CAS on the cell we have fails we modify our probe value in hope of hitting an empty/uncontended slot.

The above design (as I read it) is about compromise between contention, CPU cycles and memory. We could for instance simplify it by allocating all the cells upfront and set the number of cells upfront to the number of CPUs (nearest power of 2 >= NCPU to be exact). But imagine this counter on a 128 CPU beast, we've just allocated 128 * (56 * 2 + 8 + change) bytes -> 14k for a counter that may or may not be contended. We pay for our frugal approach every time we hit contention between threads. On the plus side, every time we grow the cells array the likelihood of collision goes down. To further help things settle, threads will change cells to eventually find less contended cells if their probe value is an issue.
Here's a discussion between Doug and Nathan Raynolds on the merits of having per CPU counters which may help shed light on the design further. I got the feeling from reading it that the cells are an abstraction of the CPU counters, and given no facility in the language to discover processor ID on the fly it seems like a good approach. Doug also mentions @Contended as a means to replace the manual padding, which is probably on it's way.
I can think of 2 ways to improve the above slightly:

Use an array of longs instead of cells, padding by leaving slots of the array empty. This is essentially the approach taken by Cliff Click here: ConcurrentAutoTable (One reviewer pointed out the CAT vs Doug pun...). This should reduce the space requirement (only need half the padding) and will inline the values in the array (one less pointer to chase). If we had value type/structs array support in Java we would need this. The even stride in memory should also improve the read performance.

Under the assumption of cells correctly reflecting the CPU affinity of threads we could co-host counters in the same cell. Of-course if the same cell is hit from several processors we've just re-introduced false sharing :(... maybe hold off on that one until we have processor ID.

Assuming all threads settle on top of their respective CPUs and no contention happens, this implementation is still bound by doing volatile writes and the logistics surrounding the cell acquisition.
How does it perform (benchmark code is here)?

LongAdderCounterBenchmark.rw:get(N=1) - 10.600 nsec/op

LongAdderCounterBenchmark.rw:inc(N=1) - 249.703 nsec/op

LongAdderCounterBenchmark.rw:get(N=11) - 752.561 nsec/op

LongAdderCounterBenchmark.rw:inc(N=11) - 44.696 nsec/op

LongAdderCounterBenchmark.rw:get(N=23) - 1380.936 nsec/op

LongAdderCounterBenchmark.rw:inc(N=23) - 38.209 nsec/op

Nice! low cost updates and high (but acceptable) read cost. This better reflects our use case, and if your are implementing performance counters in your application you should definitely consider using a back ported LongAdder. This is the approach taken in in Yammer Metrics project. Their Counter holds a back ported LongAdder whose performance is (code here):

Perhaps because of the changes between the 2 versions, or because LongAdder was designed to run on a JDK8 VM the results favour the backport over the JDK8 version when running on a JDK7 install. Note that the 1 writer/reader case performs badly, far worse than AtomicLong. On the high contention path this reverts to expected behaviour.

For completeness I tested how Cliff's CAT (I took the code from the link provided earlier) performs (here's the benchmark). CAT has a nice _sum_cache field which is used to avoid reading the table if it has not been invalidated. This saves the reader alot of work at theoretically little cost for the writers. The results I got running the benchmark varied wildly with get() looking great when inc() wasn't and get() performing very badly when inc() performs superbly. I suspect the shortcut above to be at the heart of this instability, but have not had time to get to the root of it. I exclude the results as they varied too wildly from run to run to be helpful.

Why Should You Care?

Why indeed? Why are Doug and Cliff worried? if you've watched the Future Of The VM talk featuring those very same dudes, you'd have noticed the future has many many many CPUs in it. And if we don't want to hit a wall with our data structures which worked so well back when we had only 4 CPUs to worry about then we need to get cracking on writing some mighty parallel code :-)
And if it turns out your performance counters are in fact the very reason you can't perform, wouldn't that be embracing? We need to be able to track our systems vital stats without crippling these systems, here is a paper from Mr. Dave Dice (follow his blog, great stuff) and friends looking at scalable performance counters. Let me summarise for my more lazy readers:

"commonly-used naive concurrent implementations quickly become problematic, especially as thread counts grow, and as they are used in increasingly Non-Uniform Memory Access (NUMA) systems." - The introduction goes on to explore AtomicLong type counters and non-threadsafe hacks which lose updates. Both are unacceptable. Using LOCK ADD will not save you - "Experiments conﬁrm our intuition that they do not adequately addresses the shortcomings of
the commonly used naive counters. "

Section 2.1 "Minimal Space Overhead" while interesting is not really an option for a Java hacker as it requires us to know which NUMA node we are on. In future, when processor ID is available to us we may want to go back to that one. Further more conflicts are resolved by letting updaters wait.

Section 2.2 "Using A little More Space" outlines something similar to LongAdder above, but again using NUMA node IDs to find the slot and contend locally on it. It then boils down to these implementations - "suffer under high contention levels because of contention between threads on the same node using a single component." - not surprising given that a NUMA node can have 12 logical cores or more hitting that same component, CASing away.

"won't be nothing you can't measure anymore"

Section 3 onward are damn fascinating, but are well beyond the scope of this humble post. Dice and friends go on to explore statistical counters which using fancy maths reduce the update rate while maintaining accuracy. If you find 'fancy maths' unsatisfying, JUST READ IT!!! The end approach is a combination of statistical counters and the original LongAdder style counters. It is balancing CPU cycles vs. the cost of contention for the sake of memory efficiency and finding the right balance.

The reason I mention this article is to highlight the validity of the use case. Writes MUST be cheap, reads are less of a worry. The more cores are involved, the more critical this issue becomes. The future, and in many ways the present on the server side, is massively parallel environments. We still need counters in these environments, and they must perform. Can we get better performance if we were to give up some of the features offered by LongAdder?

TLC: Love does not stand sharing

What the LongAdder is trying to avoid, and the above article is side stepping as a viable option, is having a counter copy per thread. If we could have ProcessorLocal counters then that would probably be as far as we need to go, but in the absence of that option should we not consider per thread counters? The down side as one would expect is memory consumption, but if we were to off-balance the padding against the duplicates then there are use cases where per thread counters may prove more memory efficient. The up side is that we need not worry about contention, and can replace the costly CAS with the cheap lazySet (see previous post on the topic). Even if we pad, there are ways to minimize the padding such that all counters share a given thread's padding. If you work in an environment where you typically have fixed sized thread pools executing particular tasks, and performance is critical to what you do then this approach might be a good fit.
The simplest approach is to use ThreadLocal as a lookup for the current thread view of the shared counter and have get() sum up the different thread copies. This ThreadLocalCounter (with padding) scales surprisingly well:

There are minor details worth considering here such as how ThreadLocal copies are added to the pool of counters and how to deal with dead threads. While joining is easily done via ThreadLocal.initialValue, there is sadly no cleanup callback to utilise on on Thread termination which means dead counters can collect in the pool. In an environment in which threads live short and careless lives only to be replaced by equally foolish threads this approach will not scale as counters are left stuck in the array. A simple solution here is to have the pool check for dead counters when new threads join. Counters may still be left in the pool long after the Thread died, but it will do a rudimentary check for leftovers on each thread added. Another solution is to do the check on each get(), but this will drive the cost of get() up. Yet another approach is to use WeakReferences to determine counters dropping off. Pick whichever suits you, you could even check on each get if it's seldom enough. This post is getting quite long as is so I'll skip the code, but here it is if you want to read it.

For all it's simplicity the above code delivers a triple the performance of LongAdder for writers at the cost of allocating counters per thread. With no contention LongAdder will deliver the cost of a single AtomicLong, but TLC will cost the same. The get() performance is similar to LongAdder, but note that contended or not the get() performance of the TLC is constant and gets worse with every thread added, where the LongAdder will only deteriorate with contention, and only up to the number of CPUs.

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A more specific solution requires us to add a counter field to our thread. This saves us the lookup cost, but may still leave us exposed to false sharing should our thread field be updated from other thread (read this concurrency-interest debate on making Thread @Contended). Here's how this one performs (benchmark is here):

This is 10 times faster than LongAdder on the inc(), and 700 times faster than AtomicLong when the shit hits the fan. If you are truly concerned about performance to such an extreme this may be the way to go. In fact this is exactly why ThreadLocalRandom is implemented using a field on Thread in JDK8.

A cooking pun!

Now, cooking your own Thread classes is certainly not something you can rely on in every environment. Still it may prove an interesting option for those with more control over their execution environments. If you are at liberty to add one field, just bunch the lot with no padding between them on your thread and pad the tail to be safe. As they are all written to from the same thread there is no contention to worry about and you may end up using less space than the LongAdder.

Summary

For all counters results reflect 1/11/23 writers busily updating a counter while a single reader thread reads:

LongAdder -> A table of counters -> slow get(10ns/752ns/1380ns) but faster inc(105ns/40ns/38ns). get() is slower as contention grows, inc() is slow when no cells are present, but settles on a fast figure once it gets going. Memory consumption increases with contention, but little overhead for no contention. Cells are padded so increase has 122b overhead.

ThreadLocalCounter -> Every thread has a counter in it's ThreadLocal map -> slow get(61ns/725ns/1584ns) but even faster inc(15ns/12ns/16ns). get() is slower as the number of threads grow, inc() is pretty contant and low cost. Memory consumption increases with every thread added, regardless of contention. Counters are padded so each has 122b overhead. It is suggested you use a shared counters class to amortise the padding cost.

ThreadFieldCounter -> Every thread has a counter field -> get(25ns/483ns/952ns) is slower than AtomicLong, but better than the rest. The fastest inc(4ns/3ns/3ns). get() is slower as the number of threads grow, inc() is contant and low cost. Memory consumption increases with every thread added, regardless of contention. Counters are not padded but you may want to pad them.

I believe that for certain environments the counter per thread approach may prove a good fit. Furthermore for highly contended counters, where LongAdder/similar will expand I believe that this approach can be extended to provide a more memory efficient alternative when the thread sets are fixed or limited and a group of counters is required rather then just one counter.

26 comments:

When Java eventually gets round to adding LOCK XADD support for atomic increments things will get much better. Hopefully Java 8. For many algorithms an actually sequence is required so the tricks discussed are not applicable.

I'd be a big fan of having an intrinsic to ask what core a thread is running on, e.g. CPUID instruction, from this we can have core level counters and other data structures that have no contention and perfectly scale.

Absolutely right, should have highlighted more prominently that LongAdder/TLC/CAT etc all lose the function of being a usable seq. We sacrifice funtionality to gain performance ;-)LOCK XADD by itself will not save the day I don't think, but as a replacement to LOCK CMPXCHG in incrementing the striped counters it should improve performance.Having a CPUID intrinsic would be great, and while we are asking I think we should have an interface for querying the CPU/NUMA layout and setting thread/data affinity (so CpuLocal and NumaNodeLocal). That would also open up the range of solutions discussed in Dave Dice's article to us.

AtomicLong.incrementAndGet/getAndIncrement do not compile to LOCK INC/XADD as far as my assembly output goes with JDK7.With JDK8 things are different, where indeed you are right and they are translated into a LOCK XADD instruction (via the new Unsafe method getAndAddLong). Bring on JDK8 :-)What other modern JVMs did you have in mind?Still no CPUID intrinsic though, and as mentioned, LOCK XADD is not a solution the the scalable counter problem.

The LOCK INC and DEC are atomic across CPUs by specification on Intel. You don't need the CPUID if you have atomic instructions. You don't need CPUID support, a simple ThreadLocal works fine if you are using a 'prefix' id to avoid contention.

Furthermore, there are other JVMs, notably the IBM ones that already replace many of the intrinsics before JDK8, including the atomic operations with native CPU instructions when available, so using an ATomicLong is incredibly cheap, and if that is not cheap enough, a ThreadLocal with 'id prefix' is the cheapest.

I admit ignorance when it comes to the IBM JVM, the little experience I had did not mark it as the best choice performance wise, but that may have been my particular use case. Using intrinsics where possible is a common feature to all JVMs, but which intrinsics are available/are used changes from one to one, and release to release.I get the feeling you are mixing 2 unrelated things here:1. Atomic instructions such as LOCK XADD etc are not a solution to scalable counters because they still end up bottlenecking on the shared counter. It is because they are atomic that they are not a solution. If you do go for an Atomic*, you might want to consider padding it to avoid false sharing.2. CPUID allows you to have CPU local structures. The number of CPUs is constrained, the number of threads less so. By having a means to write CPU local structures you get all the benefits of the ThreadLocal solution without the penalty of uncapped memory consumption. If a thread can write to it's cpu local counter at the same/lower cost than writing to a thread local structure then it's quite a neat solution. Having affinity for the duration of the increment at least is required, or some other special instruction. I'm not sure how aggregation is any different than the thread local case. There's no need for the memory flush if the writes are ordered. The CPUID is a means to not requiring the atomic instruction.

I think you'll find the overhead of LOCK INC/DEC is far lower than what you would actually be counting... so for counters it suffices. This highlights the problems with micro-benchmarking....

Also, I would argue that if there is "contention" and you are worried about nanosecs, then the actual value is going to meaningless, and relative values are more important (and thus dropping "some" counts is fine i.e. statistical counters). The LOCK INC/DEC is very efficient

If it is for sequential identifiers, and the overhead for LOCK INC/DEC is too great, then the number of objects being generated is astronomical, and you'll probably run out of memory first, or there is really no contention to begin with.

LOCK ADD/INC/DEC by virtue of their atomicity are far more expensive(50 odd cycles) than a local counter increment (1 cycle). If you have 128 cores hitting the counter, the operation cost is not the issue, the cache coherency overhead is. You will be contending on the cache line and that is the real bottleneck for the non-striped counters. That is at least my understanding and the same opinion is echoed by Mr. Dice and friends in the article quoted above. Still, numbers are more valuable than words here and if you can share some code which demonstrates you opinion I'm happy to learn.I agree that micro-benchmarks have their issues, but you must agree that opinion with no evidence has some issues too. Demonstrate to me that LOCK ADD is the solution to scalable counters and I'll join you in shouting it from the roof tops hand in hand ;-)The above post gives up the functionality of sequential identifiers upfront, so creating objects to be identified is irrelevant. The speed of allocation is not relevant.The link looks interesting, thanks :-) but again I don't want to lock, I want to count without getting in the way of others counting.

Then a non-shared local counter is going to be fastest, since the aggregation is only going to be on an interval, so the memory request flush is very rare, and the volatile reads for the flag are extremely cheap.

As far as the cache-coherency, this is an active area of research for highly parallel systems anyway - why do this work in user land, when the CPU/bus can (will eventually) do it more efficiently? Write it simply using a known abstraction then the JVM/hardware vendor can fix.

I think you are going to find that whatever counter method you choose, the overhead/cost pales in comparison to the actual work being done (in a real system and its worth being counted), and is negligible - unless your "real work" is testing counters, which goes back to the micro-bench-marking issues...

1. That's exactly the end conclusion of the article, I'm glad we agree :-)2. There's a difference between can and 'will-eventually', why wait? if you need the performance use the tool you have now rather then revel at the beauty of your slow design ;-). If/when they fix it, there will be other means of achieving even better performance. The abstraction is also wrong in the case of Atomic* as you will forever be paying for atomicity, which you don't want. If there was a hardware striped counter coming, or some method of queuing up updates for a joint counter without suffering contention, then I'm very interested in hearing about it. LongAdder being added to the JDK seems to suggest the folks building the JDK think you might need it for some time now.3. The overhead for incrementAndGet() on Atomic* grows with contention. In the example above it grows to the point of costing 2.1 microseconds. This is allot of time for a trading system, but also this is allot of time your code could be spending doing something more exciting than incrementing a counter. 2.1 microseconds are 700 times more than incrementing a local counter that is easily accessible. It's not negligible to some people. Weather you should both hacking your own when LongAdder performs fairly well at 40 nanos is a different kettle of fish. I think the impact of injecting your code with CAS is not visible on a benchmark but may do some damage on a real system.Thanks for the debate :-), made me double check my assumptions at the very least. I agree with your earlier point that the best implementation would be to carry the counter on the call stack rather than embed it in Thread, this is sadly a bit of an annoying handicap for the users of you API if they need to keep passing the thread context into each and every method, just in case it needs to update a counter.

One other thing I noticed... I don't think the tests are representative of the "real world". If you look at the above "high" timings for the n=1 case, you state that this is due to contention. Well, I think in most "counting" uses, there reading is VERY rare - that is, the counts are updated frequently (say, contended locks), but the reading is only done when requested (jstat, profiler read, etc.). Given this, the per thread counting structure is always going to be the best solution. Then it becomes a matter of needed accuracy, possible contention, and whether a lossy solution is acceptable. Unless the threads are "paused" (which is difficult to pull off in a real world system with timers, etc.), the reader is always "out of sync" with the writing anyway, and the counters are typically only useful in a macro sense.

I'm also OK with #2 if done with enough abstraction so the code can be easily shielded (and removed later!), but that potentially adds an overhead as well. Unless you are doing HPC, or low-level systems work (or in our case high frequency trading systems), fighting for micro/nano secs just isn't worth the effort to most software projects. Clear understandable design is always going to lead to greater efficiency in terms of product maintainability, and flexibility (take advantage of these new hardware and JVM capabilities !)

I don't think the reads are interfering with the writes much, but would have to re-run to verify. If they do then I agree with you that the tests are skewed that way. Previous similar experiments lead me to believe that is not an issue here.The timings for N=1 represent no write contention, if the post seems to suggest that there is contention for the N=1 case then that is a mistake. I'll re-read and look for that, but if you can point me at the issue I'll correct it.Thanks again.

Not sure why you think anything is wrong...For the ThreadFieldCounter inc() is a trivial write to a field on the Thread.current(). It's cost remains constant. For the get() the cost is as expected from running in a loop looking up instances of Threads in an array. No issueFor the LongAdder the N=1 case is an edge case, it's not handled via the cells array but via a field on the class. It's like an AtomicLong but with allot more logic around it and the cost is in a ballpark reflecting that. For N=11 the cost of inc() goes down as we are now hitting the expected behaviour but the cost of get goes up as we have to iterate through the cells and sum them. As N=11 there are probably 16 cells, because the size of cells is always a power of 2.The results were consistent, I ran the benchmarks many times to verify them. They also sit well with the other LongAdder implementation results. No issue.When you say something seems wrong, do you have an independent measurement which contradicts the above? what do you think is wrong? based on what?

What is the reasoning for an N=11 inc performing better than an N=1 inc ? I can see that get() times will become increasing larger (more buckets to sum), and inc should be "nearly constant" in a very efficient solution, but why would are more gets more efficient than less??? I thought I read in your articilar that it was due to contention on the single bucket between read and write (in the n=1 case), and that is what my point was - that the test is not "real world" since there should really be no read contention on the write bucket for a counter.

Btw, I re-read all of the posts, and I just want to be clear - I KNOW you are correct in terms of the approach, and that highly parallel systems are certainly the future so this becomes more of an issue, but in summary I would argue that if you are worried about nanosecs in contented atomic increments (if you read the doc I referenced, uncontended atomic increments are very cheap), then you are probably updating the counter at such a rate, that the counter is probably meaningless (as an absolute value), and that lossy solutions (simple increment on a volatile) will provide almost the same information with no real overhead (for example in a system that multiple threads are updating the same counter, unless there is only a single thread per cpu, the context switches and cache flushes (and other memory barrier ops) are going to evict the cached value to main memory often enough that the shared "lazy" counter will provide enough information to be useful.

In the order of the points raised above:- What is the reasoning for an N=11 inc performing better than an N=1 inc?I'm not sure my answer is correct, but that doesn't mean it's not happening. I would expect this is down to branch mis-predictions, but have not tested. You can run the benchmarks a use perf stats to validate/contradict this theory. I think the measurement is correct, but if you want to validate it using a hand rolled micro-benchmark or some other method that would also be a valid way to check this out. I still can't find anything to there is contention in the N=1 case anywhere in the post.- If you are worried about nanosecs in contented atomic increments... the counter is probably meaninglessThe use case outlined is that I want to monitor the application, and that I care about nanoseconds in the business logic. Why does that make the counter meaningless? I actually encountered this issue as part of my day job. The updates to the counter need not be atomic, and the accuracy of the read value is assumed to be slightly out of date, but that does not mean it's meaningless. The lossy solution you describe leads to an inaccuracy that is not workable, please read Mr. Dice's article for further details on the topic. The bottom line is that this approach is not considered useful.- the context switches and cache flushes (and other memory barrier ops) ... etcThe bottom line is in that in the real world your application will be as good as you care to make it. If you have a requirement to expose performance counters that should not stop you from building an application that makes the most of the hardware you have. There is little to stop you from building a CPU per thread type application on a 128 core machine, and if you accept that as the premise then having concurrent data structures to support that reality is important, or at least interesting.

Just a (maybe naive) question. Usually stats are read once or twice a second, so isn't it much better to use thread locals or dedicated counters per thread and accumulate the values once stats are read ?

Yes, the post demonstrates exactly that. If you reading code is infrequent and low priority then either the thread local or thread field version are your best option. Note that this may not be so great when you got 1000s of thread flying about with counters... A pleasure to have you reading :)